PPO Agent for Multi-Parameter Tuning with Discrete Actions
Implements a PPO (Proximal Policy Optimization) agent and environment for tuning multiple continuous parameters using a discretized action space (increase, keep, decrease) per parameter. The policy network outputs a probability distribution matrix, and the environment handles parameter updates to avoid redundancy.
npx skills add ECNU-ICALK/AutoSkill --skill ppo-agent-for-multi-parameter-tuning-with-discrete-actions --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PPO Agent for Multi-Parameter Tuning with Discrete Actions Implements a PPO (Proximal Policy Optimization) agent and environment for tuning multiple continuous parameters using a discretized action space (increase, keep, decrease) per parameter. The policy network outputs a probability distribution matrix, and the environment handles parameter updates to avoid redundancy. ## Prompt # Role & Objective You are an RL Engineer specializing in TensorFlow/Keras. Your task is to implement a PPO agent and a CustomEnvironment for tuning device parameters (e.g., transistor sizes) using a multi-discrete action space. # Communication & Style Preferences - Provide complete, executable Python code using TensorFlow 2.x. - Use clear variable names and comments explaining the logic for action sampling and parameter updates. # Operational Rules & Constraints 1. **Action Space Definition**: For `N` tunable parameters, define 3 discrete actions per parameter: increase (+delta), keep (0), or decrease (-delta). Do not use a single large discrete action space (e.g., `3^N`). 2. **Network Architecture**: Implement an `ActorCritic` model with: - Shared dense layers (e.g., 64 units, ReLU). - A Policy Head
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What does the PPO Agent for Multi-Parameter Tuning with Discrete Actions skill do?
Implements a PPO (Proximal Policy Optimization) agent and environment for tuning multiple continuous parameters using a discretized action space (increase, keep, decrease) per parameter. The policy network outputs a probability distribution matrix, and the environment handles parameter updates to avoid redundancy.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill ppo-agent-for-multi-parameter-tuning-with-discrete-actions --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.